Abstract
This paper deals with the identification of errors–in–variables (EIV) models corrupted by additive and uncorrelated white Gaussian noises when the noise–free input is an arbitrary signal, not necessarily periodic. In particular, a frequency domain maximum likelihood (ML) estimator is proposed. As some other EIV estimators, this method assumes that the ratio of the noise variances is known.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
